Exploring the Equity of Traditional and Ride-Hailing Taxi Services during Peak Hours

Pan, Renbin; Yang, Hongtai; Xie, Kun; Yu, Wen

doi:10.1177/0361198120928338

Cited by 32 publications

(11 citation statements)

References 27 publications

(33 reference statements)

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“…Among the four functions, fixed Gaussian and adaptive bi-square kernel functions are the most extensively used in the existing literature [42]. Furthermore, the selection of the optimal kernel function and the bandwidth often relies on the corrected Akaike information criterion (AICc) [43].…”

Section: Geographically Weighted Regression (Gwr) Modelsmentioning

confidence: 99%

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Yang

Liu

Liang

et al. 2021

IJGI

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Population aging has become a notable and enduring demographic phenomenon worldwide. Older adults’ walking behavior is determined by many factors, such as socioeconomic attributes and the built environment. Although a handful of recent studies have examined the influence of street greenery (a built environment variable readily estimated by big data) on older adults’ walking behavior, they have not focused on the spatial heterogeneity in the influence. To this end, this study extracts the socioeconomic and walking behavior data from the Travel Characteristic Survey 2011 of Hong Kong and estimates street greenery (the green view index) based on Google Street View imagery. It then develops global models (linear regression and Box–Cox transformed models) and local models (geographically weighted regression models) to scrutinize the average (global) and location-specific (local) relationships, respectively, between street greenery and older adults’ walking time. Notably, green view indices in three neighborhoods with different sizes are estimated for robustness checks. The results show that (1) street greenery has consistent and significant effects on walking time; (2) the influence of street greenery varies across space—specifically, it is greater in the suburban area; and (3) the performance of different green view indices is highly consistent.

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Section: Geographically Weighted Regression (Gwr) Modelsmentioning

confidence: 99%

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Yang

Liu

Liang

et al. 2021

IJGI

Self Cite

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“…where w in = the spatial weight value between grid i and others. In this study, the commonly used adaptive bi-square kernel is used to calculate the spatial weighting matrix, which simulates the spatial effect of surrounding observation through adaptive distance decay in the bandwidth range (45,46). It is worth noting that the bandwidth selection is also essential because it greatly influences coefficient estimation.…”

Section: Methodsmentioning

confidence: 99%

Influence of Built Environment on Subway Trip Origin and Destination: Insights Based on Mobile Positioning Data

Wang

Pan

et al. 2022

Transportation Research Record

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An accurate understanding of the relationship between subway trips and the built environment is crucial to meet people’s travel demands and promote the coordinated development of urban land. However, existing literature mainly examines this relationship at the station level, ignoring the variations between the surrounding areas of the station. Therefore, the present study aims to explore the influencing factors and spatial variations of subway trips at the grid level. First, a method was proposed to extract the subway trips using mobile positioning data in Chengdu, China. Then, two geographically weighted regression (GWR) models were adopted to examine the spatially varying relationships between the subway trip origin and destination and selected explanatory variables. The results show that the hotel, company, residential, tourist, bus, subway, road density, and transfer stations variables positively affect trip origin and destination. However, distance to the nearest subway station has a negative impact. Besides, the goodness of fit of the GWR model is better than that of the global regression model, indicating that the influence of the built environment on trip origin and destination varies across space. This study can guide planning departments and transportation agencies to implement target policies and create a convenient travel environment at the micro-level.

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“…Hu et al [17] used the generalized additive mixed effects model (GAMM) to investigate the nonlinear relationship between determinants and the attractiveness of car sharing. Since spatial autocorrelation is always observed when dealing with spatial data, spatial econometric models are usually used to deal with this issue [15,33,34]. Gan et al [33] applied the spatial error model and found the factors that significantly influence station-level ridership while controlling for spatial autocorrelation.…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

Yang

et al. 2021

Journal of Advanced Transportation

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Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R 2 values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models.

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Exploring the Equity of Traditional and Ride-Hailing Taxi Services during Peak Hours

Cited by 32 publications

References 27 publications

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Influence of Built Environment on Subway Trip Origin and Destination: Insights Based on Mobile Positioning Data

Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

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